TY - GEN

T1 - The dangers of sparse sampling for uncertainty propagation and model calibration

AU - Hemez, François M.

AU - Atamturktur, H. Sezer

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2011

Y1 - 2011

N2 - Activities such as sensitivity analysis, statistical effect screening, uncertainty propagation, or model calibration have become integral to the Verification and Validation (V&V) of numerical models and computer simulations. Because these analyses involve performing multiple runs of a computer code, they can rapidly become computationally expensive. For example, propagating uncertainty with a 1,000 Monte Carlo samples wrapped around a finite element calculation that takes only 10 minutes to run requires seven days of single-processor time. An alternative is to combine a design of computer experiments to meta-modeling, and replace the potentially expensive computer simulation by a fast-running surrogate. The surrogate can then be used to estimate sensitivities, propagate uncertainty, and calibrate model parameters at a fraction of the cost it would take to wrap a sampling algorithm or optimization solver around the analysis code. In this publication, we focus on the dangers of using too sparsely populated design-of-experiments to propagate uncertainty or train a fast-running surrogate model. One danger for sensitivity analysis or calibration is to develop meta-models that include erroneous sensitivities. This is illustrated with a high-dimensional, non-linear mathematical function in which several parameter effects are statistically insignificant, therefore, mimicking a situation that is often encountered in practice. It is shown that using a sparse design of computer experiments leads to an incorrect approximation of the function.

AB - Activities such as sensitivity analysis, statistical effect screening, uncertainty propagation, or model calibration have become integral to the Verification and Validation (V&V) of numerical models and computer simulations. Because these analyses involve performing multiple runs of a computer code, they can rapidly become computationally expensive. For example, propagating uncertainty with a 1,000 Monte Carlo samples wrapped around a finite element calculation that takes only 10 minutes to run requires seven days of single-processor time. An alternative is to combine a design of computer experiments to meta-modeling, and replace the potentially expensive computer simulation by a fast-running surrogate. The surrogate can then be used to estimate sensitivities, propagate uncertainty, and calibrate model parameters at a fraction of the cost it would take to wrap a sampling algorithm or optimization solver around the analysis code. In this publication, we focus on the dangers of using too sparsely populated design-of-experiments to propagate uncertainty or train a fast-running surrogate model. One danger for sensitivity analysis or calibration is to develop meta-models that include erroneous sensitivities. This is illustrated with a high-dimensional, non-linear mathematical function in which several parameter effects are statistically insignificant, therefore, mimicking a situation that is often encountered in practice. It is shown that using a sparse design of computer experiments leads to an incorrect approximation of the function.

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U2 - 10.1007/978-1-4419-9834-7_48

DO - 10.1007/978-1-4419-9834-7_48

M3 - Conference contribution

AN - SCOPUS:80051486847

SN - 9781441998330

T3 - Conference Proceedings of the Society for Experimental Mechanics Series

SP - 537

EP - 556

BT - Structural Dynamics - Proceedings of the 28th IMAC, A Conference on Structural Dynamics, 2010

PB - Springer New York LLC

ER -